Pathway to Biostatistics: My Journey and Advice
When people ask if I had always known that I wanted to work in medical research, I can’t help but laugh.
Funny enough, biology was one of my least favorite subjects in high school—so much so that I avoided it entirely in my undergraduate years and immersed myself in math and social science classes instead. At the time, I felt like biology was just memorizing diagrams and I couldn’t see how it connected to anything I cared about. Somewhere along the way, I realized that biology was tied into everything I found interesting—it was deeply woven into our society, history, and the systemic biases I am was (and am!) passionate about.
My (Nonlinear) Academic Path
When I graduated in 2018 with an undergraduate degree in statistics and a minor in psychology (with a heavy emphasis on research), I imagined myself working in psych or sociology research, policy analysis, or something else community-focused. However, the only job offer I got was in a very different world: orthopaedic surgery.
Admittedly, I was hesitant to take the job. I had to Google what “orthopaedic surgery” was (for reference, it specializes in the musculoskeletal system) and I had no background in medicine. Even though I felt extremely out of my depth, I took the role as a medical research data analyst.
I fell in love with the job almost immediately. To my surprise, a lot of the research I worked on intersected with the topics I cared about—barriers to access, disparities in patient outcomes, and how a patient’s psychological state is affected by chronic health conditions. The research I was working on could directly contribute to changes in the standard of care.
After a year in this role, I wanted to be better equipped to tackle these questions so I enrolled in a part-time public health master’s program at the end of 2019 while continuing to work full-time.
Just a few months later, the COVID-19 pandemic hit. Suddenly, public health wasn’t abstract anymore—everything I was learning in class was unfolding around me in real time. The data points I had been analyzing weren’t just numbers anymore. They had names, faces, and families. This forever affected how I approach my work and reinforced why I’m passionate about medical research.
After completing my master’s degree, I was promoted to biostatistician. I took on a larger part in designing studies, using more advanced statistical methods, and writing research papers. I’m still in this role today, continuing to grow my skills and contribute to research, while also starting to educate others about biostatistics and data analysis.
What I’ve Learned Along the Way
Looking back, one of the biggest lessons I’ve learned is that you don’t need to have a perfect or traditional background for your dream job. I didn’t love biology in high school and I didn’t start out aiming for medical research, but a willingness to learn and try new things opened up doors for me.
Don’t be afraid to take unconventional paths. Your first job may not be data analyst or data science job you were hoping for, and that’s okay. What matters most is gaining experience, building skills, and building connections. Explore ancillary jobs like data coordinator, research assistant/coordinator, database administrator, etc. These roles can offer valuable exposure to data, terminology, and workflows.
“Soft” skills are just as important as technical skills. While a strong background in coding, statistics, and visualization are essential, being able to communicate your findings to a non-technical audience is just as important. In addition, other “soft” skills like critical thinking, curiosity, and adaptability are crucial in navigating complex problems and collaborating with large teams.
Create a portfolio. Providing a sample of your work, whether it’s a dashboard, website, GitHub repository, or well-documented Markdown report, can help you stand out. This is an effective way to demonstrate technical abilities, your problem-solving approach, and communication skill all in one place.
Never stop learning. The world of data evolves quickly, so stay curious. Take courses, attend workshops, or even join an online community. There are many free or low-cost resources out there, like auditing a Coursera course or reading a blog post. Practice analysis skills on data from sites like Kaggle, GitHub, or Data.gov.
Finally, remember that everyone’s path is different. If I had waited for the “perfect” job, I would have missed out on an incredibly rewarding career. Whether you stay in your first role or pivot to something new within a year, focus on building skills and making the most of it. Every experience is valuable, even if it doesn’t seem like a part of the plan.